System and method for analyzing web content

ABSTRACT

A system and method are provided for identifying active content in websites on a network. One embodiment includes a method of classifying web content. In one embodiment, the classifications are indicative of active and/or malicious content. The method includes identifying properties associated with the web page based at least partly on the content of the web page and storing said properties in a database of web page properties. The method further includes comparing at least one definition to properties stored in the database of web page properties and identifying the web page with at least one definition based on comparing said definition with said stored properties. The method further includes identifying the web page with at least one category associated with the at least one definition, wherein said category is indicative of active content associated with the web page. Other embodiments include systems configured to perform such methods.

RELATED APPLICATIONS

This Application is related to U.S. patent application Ser. No. ______,filed on even date, Attorney Docket No. WEBSEN.083A, which is herebyincorporated by reference

BACKGROUND OF THE INVENTION

1. Field of the Invention

This application relates to data and application security. Inparticular, this application discloses systems methods of collecting andmining data to determine whether the data is associated with maliciouscontent.

2. Description of the Related Technology

Traditionally, computer viruses and other malicious content were mostoften provided to client computers by insertion of an infected disketteor some other physical media into the computer. As the use of e-mail andthe Internet increased, e-mail attachments became a prevalent method fordistributing virus code to computers. To infect the computer with thesetypes of viruses having malicious content, some affirmative action wastypically required by the user such as opening an infected fileattachment or downloading an infected file from a web site and launchingit on their computer. Over time, antivirus software makers developedincreasingly effective programs designed to scan files and disinfectthem before they had the opportunity to infect client computers. Thus,computer hackers were forced to create more clever and innovative waysto infect computers with their malicious code.

In today's increasingly-networked digital world, distributedapplications are being developed to provide more and more functionalityto users in an open, collaborative networking environment. While theseapplications are more powerful and sophisticated, their increasedfunctionality requires that network servers interact with clientcomputers in a more integrated manner. For example, where previous webapplications primarily served HTML content to client browsers andreceived data back from the client via HTTP post commands, many new webapplications are configured to send various forms of targeted content,such as active content, to the client computer which cause applicationsto be launched within the enhanced features of newer web browsers. Forexample, many web-based applications now utilize Active-X controls whichmust be downloaded to the client computer so they may be effectivelyutilized. Java applets, JavaScript, and VBScript commands also have thecapability of modifying client computer files in certain instances.

The convenience that has arrived with these increases in functionalityhas not come without cost. Newer web applications and content aresignificantly more powerful than previous application environments. As aresult, they also provide opportunities for malicious code to bedownloaded to client computers. In addition, as the complexity of theoperating system and web browsing applications increase, it becomes moredifficult to identify security vulnerabilities which may allow hackersto transfer malicious code to client computers. Although browser andoperating system vendors generally issue software updates to remedythese vulnerabilities, many users have not configured their computers todownload these updates. Thus, hackers have begun to write malicious codeand applications which utilize these vulnerabilities to downloadthemselves to users' machines without relying on any particular activityof the user such as launching an infected file. One example of such anattack is the use of malicious code embedded into an active contentobject on a website. If the malicious code has been configured toexploit a vulnerability in the web browser, a user may be infected orharmed by the malicious code as a result of a mere visit to that page,as the targeted content in the page will be executed on the user'scomputer.

An attempt to address the problem of malicious code being embedded inactive content is to utilize heightened security settings on the webbrowser. However, in many corporate environments, intranet or extranetapplications are configured to send executable content to clientcomputers. Setting browser settings to a high security level tends toimpede or obstruct the effective use of these types of “safe”applications. Another attempt to address the issue is to block allexecutable content using a network firewall application. This bruteforce approach also is ineffective in many environments, becauseselective access to certain types of content is necessary for softwareto correctly function.

What is needed is a system and method that allows for the detection ofmalicious web content without compromising user functionality. Further,what is needed is a system that can detect targeted content such asactive content and quickly identify and categorize its behavior, andprovide protection from the malicious content to a high Volume of clientcomputers with minimum delay.

SUMMARY OF CERTAIN INVENTIVE EMBODIMENTS

The system, method, and devices of the present invention each haveseveral aspects, no single one of which is solely responsible for itsdesirable attributes. Without limiting the scope of this invention,several of its features will now be discussed briefly.

One embodiment includes a method of classifying web content. The methodincludes receiving content of at least one web page. The method furtherincludes identifying properties associated with the web page based atleast partly on the content of the web page. The method further includesstoring the properties in a database of web page properties. The methodfurther includes comparing at least one definition to properties storedin the database of web page properties. The method further includesidentifying the web page with at least one definition based on comparingthe definition with the stored properties. The method further includesidentifying the web page with at least one category associated with theat least one definition, wherein the category is indicative of activecontent associated with the web page.

On embodiment includes a system for classifying web content. The systemincludes a database configured to properties associated with web pages.The system further includes at least one processor configured toidentify properties associated with a web page based at least partly oncontent of the web page and store the properties in the database of webpage properties. The processor is further configured to compare at leastone definition to properties stored in the database of web pageproperties, identify the web page with at least one definition based oncomparing the definition with the stored properties, and identify theweb page with at least one category associated with the at least onedefinition, wherein the category is indicative of active contentassociated with the web page.

BRIEF DESCRIPTION OF THE DRAWINGS

In this description, reference is made to the drawings wherein likeparts are designated with like numerals throughout.

FIG. 1 is a block diagram of various components of a system inaccordance with aspects of the invention.

FIG. 2 is a block diagram of a workstation module from FIG. 1.

FIG. 3 is a block diagram of a gateway server module from FIG. 1.

FIG. 4 is an example of a logging database.

FIG. 5 is an example of a URL Access Policy database table.

FIGS. 6A and 6B are examples of categorized and uncategorized URLs,respectively.

FIG. 7 is a block diagram of a database management module from FIG. 1.

FIG. 8 is a block diagram of a collection system from FIG. 7.

FIG. 9 is a block diagram of a collection module from FIG. 8.

FIG. 10 shows a honey client system according to some aspects of theinvention.

FIG. 11 is an example of URL-related data collected by the collectionmodule from FIG. 9.

FIG. 12 is a block diagram illustrating a scoring and categorizationmodule from FIG. 7.

FIG. 13A is an example of a properties table.

FIG. 13B is an example of a processed web page properties table.

FIG. 13C is an example of a definitions table.

FIG. 14 is a block diagram illustrating one embodiment of a trainingmodule from FIG. 7.

FIG. 15 is a block diagram illustrating one embodiment of an activeanalysis system from FIG. 12.

FIG. 16 is a flowchart describing how URLs may be handled in the gatewayserver module in one embodiment.

FIG. 17 is a flowchart describing how URLs may be handled by the gatewayserver module in conjunction with the policy module according to certain

FIG. 18 is a flowchart describing the how the collection system mayhandle a URL within the gateway server module.

FIG. 19 is a flowchart describing the how the collection system mayhandle a URL within the database management module.

FIG. 20 is a block diagram of a data mining system.

FIG. 21 is a flowchart illustrating one embodiment of a method ofcategorizing URLs within the database management module.

FIG. 22 is a flowchart illustrating one embodiment of a method ofidentifying properties of a URL in the method of FIG. 21.

FIG. 23 is a flowchart illustrating one embodiment of a method ofcategorizing URLs based on URL properties in the method of FIG. 21.

FIG. 24 is a flowchart illustrating one embodiment of a method ofidentifying properties used in categorizing URLs in the methods of FIGS.22 and 23.

DETAILED DESCRIPTION OF CERTAIN INVENTIVE EMBODIMENTS

The following detailed description is directed to certain specificembodiments of the invention. However, the invention can be embodied ina multitude of different ways as defined and covered by the claims. Inthis description, reference is made to the drawings wherein like partsare designated with like numerals throughout.

Certain embodiments provide for systems and method of identifying andcategorizing web content, including potentially executable web contentand malicious content, that is found at locations identified by UniformResource Locators (URLs). As used herein, potentially executable webcontent generally refers to any type of content that includesinstructions that are executed by a web browser or web client computer.Potentially executable web content may include, for example, applets,executable code embedded in HTML or other hypertext documents (includingscript languages such as JavaScript or VBScript), executable codeembedded in other documents, such as Microsoft Word macros, orstylesheets. Potentially executable web content may also refer todocuments that execute code in another location such as another webpage, another computer, or on the web browser computer itself. Forexample, a HTML web page that includes an “OBJECT” element, and thus cancause execution of ActiveX or other executable components, may generallybe considered potentially executable web content regardless of thelocation of the executable components. Malicious content may refer tocontent that is not executable but which is calculated to exploit avulnerability on a client computer. However, potentially executable webcontent may also be malicious content. For example, image files havebeen used to exploit vulnerabilities in certain operating systems whenthose images are processed for display. Moreover, malicious web contentmay also refer to interactive content such as “phishing” schemes inwhich a HTML form or other web content is designed to appear to beprovided by another, typically trusted, web site such as a bank, inorder to deceive the user into providing credentials or other sensitiveinformation to an unauthorized party.

Description of System

FIG. 1 provides a top level illustration of an exemplary system. Thesystem includes a network 110. The network 110 may be a local areanetwork, a wide area network, or some other type of network. The network110 may include one or more workstations 116. The workstations 116 maybe various types of client computers that are attached to the network.The client computers 116 may be desktop computers, notebook computers,handheld computers or the like. The client computers may also be loadedwith operating systems that allow them to utilize the network throughvarious software modules such as web browsers, e-mail programs, or thelike.

Each of the workstations 116 may be in electrical communication with agateway server module 120. The gateway server module may reside at theedge of the network 110 so that traffic sent to and from the Internet112 may pass through it on its way into or out of the network 110. Thegateway server module 112 may take the form of a software module that isinstalled on a server that stands as a gateway to a wider area network112 than the network 110 to which the workstations 116 are directlyattached. Also connected to the Internet 112 is a database managementmodule 114. The database management module also may be a software module(or one or more hardware appliances) which resides on one or morecomputing devices. The database management module 114 may reside on amachine that includes some sort of network connecting hardware, such asa network interface card, which allows the database management module114 to send and receive data and information to and from the Internet1112.

Referring now to FIG. 2, a more detailed view of the workstation 116 ispresented. The workstation 116 may include a workstation module 130. Theworkstation module 130 may take the form of software installed to run onthe operating system of the workstation 116. Alternatively, theworkstation module 130 could be an application running on anothermachine that is launched remotely by the workstation 116.

The workstation module 130 may include various components. Theworkstation module may include an inventory of a local active contentmodule 132 which records all web content stored on the workstation 116.For example, the local content inventory module 132 may periodicallyinventory all local content. The inventoried data may be uploaded to thegateway server module 120 for comparison to the categorized URL/contentdatabase 146. The local content inventory module 132 may determinewhether new content is being introduced to the workstation 116 bycomparison to the inventoried local content 132.

The workstation module also may include an upload/download module 134and a URL request module 136. The upload/download module 134 may be usedto send and receive data from the network 110, through the gatewayserver module 120 and to the Internet 112. The URL request module 136receives a URL input from either a user or some system process, and maysend a request via the gateway server module 120 to retrieve the fileand/or content associated with that URL. Typically, the functions ofeach of the upload/download module 134 and the URL request module 136may be performed by a software applications such as web browsers, withInternet Explorer®, Mozilla Firefox, Opera, Safari, being examples ofbrowsing software well-known in the art. Alternatively, the functions ofthe modules may be divided among different software applications. Forexample, an FTP application may perform the functions of theupload/download module 134, while a web browser my perform URL requests.Other types of software may also perform the functions of theupload/download module 134. Although these types of software aregenerally not desirable on a workstation, software such as Spyware, orTrojan Horses may make requests to send and receive data from theInternet.

The workstation module 130 may be in communication with the gatewayserver module 120. The gateway server module 120 may be used to analyzeincoming and outgoing web traffic and to make various determinationsabout the impact the traffic may have on the workstations 116. Referringnow to FIG. 3, an example of the gateway server module 120 is provided.The gateway server module 120 is in two way communication with theworkstation 116. It may receive file uploads and downloads and URLrequests from the workstation module 130. The gateway server module 120is also in two way communication with the Internet 112. Thus, requestsoriginating within the workstations 116 of the network 110 may berequired to pass through the gateway server module 120 as they proceedto the Internet. In some embodiments, the gateway server module 120 maybe integrated with some firewall hardware or software that protects thenetwork 110 from unauthorized intrusions from the Internet 112. In otherembodiments, the gateway server module 120 may be a standalone hardwareappliance or even a software module installed on a separate gatewayserver residing at the network gateway to the Internet 112.

As discussed above, the gateway server module 120 may receive URLrequests and upload/download data from the workstation 116 by way of theworkstation module 130. The gateway server module 120 may includevarious components that perform various functions based on the datareceived.

One feature included in the gateway server module 120 is a categorizedURL database 146. The URL database 146 may be used to store informationabout URLs including data that is associated with the URLs. Thecategorized URL database 146 may be a relational database, or it may bestored in some other form such as a flat file, an object-orienteddatabase, and may be accessed via an application programming interface(API), or some database management software (DBMS). The URL database 146may generally be used to help determine whether URL requests sent by theURL request module 136 will be permitted to be completed. In oneembodiment, the URLs stored in the URL database 146 are categorized.

The gateway server module 120 may also include a policy module 142. Thepolicy module 142 may used to implement network policies regarding howcertain content will be handled by the gateway server module 120 or by afirewall or some other security software installed within the network110. In one embodiment, the policy module 142 may be configured toprovide the system guidance on how to handle URL requests forcategorized URLs. For example, the gateway server module 120 may beconfigured to disallow URL requests that are categorized as being“Malicious” or “Spyware.” In other embodiments, the policy module 142may be used to determine how to handle URL requests that have not beencategorized. In one embodiment, the system may be configured to blockall requests for URLs that are not in the categorized URL database 146.The policy module 142 may also be configured to allow certain requestsof uncategorized URLs based on the user making the request or the timeat which the request is made. This allows the system to avoid having aone-size-fits-all configuration when such as configuration would notmeet the business needs of the organization running the gateway servermodule 120.

The gateway server module 120 may include a collection module 140. Thecollection module 140 may be a software program, routine, or processthat is used to collect data about URLs. In one embodiment, when arequest for a particular URL is received from the URL request module136, the collection module 140 may be configured to visit the URL anddownload the page data to the gateway server module 120 for analysis bycomponents of the gateway server module 120. The downloaded data mayalso be sent via the Internet 112 for delivery to the databasemanagement module 114 (as will be discussed in further detail below).

In some embodiments, the gateway server module 120 may also include alogging database 144. The logging database 144 may perform variousfunctions. For example, it may store records of certain types ofoccurrences within the network 110. In one embodiment, the loggingdatabase 144 may be configured to record each event in which anuncategorized URL is requested by a workstation 116. In someembodiments, the logging database 144 may also be configured to recordthe frequency with which a particular uncategorized URL is requested.This information may be useful in determining whether an uncategorizedURL should be of particular importance or priority and should becategorized by the database management module 114 ahead of earlierreceived data. In some embodiments, uncategorized URLs may be storedseparately in an uncategorized URL database 147.

For example, some spyware may be written to request data from aparticular URL. If many workstations 116 within the network 110 areinfected with the spyware, repeated requests to a particular URL mayprovide an indication that some anomaly is present within the network.The logging database may also be configured to record requests ofcategorized URL data. In some embodiments, categorizing requests ofcategorized URLs may be helpful in determining whether a particular URLhas been mischaracterized.

Referring now to FIG. 4, an example of the logging database 144 isdiscussed. The logging database 144 includes four columns of data. Thefirst column, “No. Page Requests” 152 is indicative of the number oftimes a particular URL has been requested by users within the network110. The second column “URL” 154 records the particular URL string thatis being logged in the logging database 144. Thus, when a URL is sent tothe logging database 144, the database may first be searched todetermine whether the URL string is already in it. If not, then the URLstring may be added to the database. In some embodiments, the collectionmodule 140 may be configured to visit the requested URL and gather dataabout the URL. The collection module 140 may retrieve the page source ofthe requested URL and scan it for certain keywords that may indicate atype of content. For example, if the page source includes“javascript://” then the page may be identified as having JavaScript.While such content is not inherently dangerous, a web page withJavaScript may have a greater chance of including malicious contentdesigned to exploit how a browser application handles JavaScriptfunction calls. In some embodiments, this data may be stored in thelogging database 144 in JavaScript column 155. The logging database mayalso receive similar information from pages that include Active-Xcontent and store that content within Active X column 156. In otherembodiments, other types of active content may be detected and storedfor java applets, VBScript, and the like.

Referring again to FIG. 3, the gateway server module 120 may furtherinclude an administrative interface module 148 or “admin module.” Theadmin module 148 may be used to allow network administrators or othertechnical personnel within an organization to configure various featuresof the gateway server module 120. In certain embodiments, the adminmodule 148 allows the network administrator or some other networkmanagement-type to configure the policy module 142.

Referring now to FIG. 5, an example of a URL access policy database 158is provided. The URL access policy database 158 may be used by thepolicy module 142 to implement policies for accessing web-based contentby workstations 116 within the network 110. In the embodiment shown theURL access policy database 158 includes a table with four columns. Thefirst column is a user column 160. The “User” column 160 includes dataabout the users that are subject the policy defined in a given row ofthe table. The next column, “Category” 162, lists the category ofcontent to which the policy defined by that row is applicable. The thirdcolumn, “Always Block” 164 represents the behavior or policy that isimplemented by the system when the user and category 166 of requestedcontent match the user and category as defined in that particular row.In one embodiment, the “Always Block” field may be a Boolean-type fieldin which the data may be set to either true or false. Thus, in the firstrow shown in the data table, the policy module 142 is configured to“always block” requests for “malicious content” by user “asmith.”

As noted above, the policy module may also be configured to implementpolicies based on different times. In the embodiment provided in FIG. 5,the fourth column “Allowed Times” 166 provides this functionality. Thesecond row of data provides an example of how time policies areimplemented. The user 164 is set to “bnguyen” and the category 162 is“gambling.” The policy is not configured to “always block” gamblingcontent for “bnguyen,” as indicated by the field being left blank.However, the time during which these URL requests are permitted islimited to from 6 PM to 8 AM. Thus, adopting these types of policiesallows network administrators to provide a certain degree of flexibilityto workstations and users, but to do so in a way that network traffic isnot compromised during typical working hours.

FIGS. 6A and 6B provide illustrations of how the categorized URLdatabase 146 may store categorized data. In one embodiment, thecategorized URLs may be stored in a two-column database table such asthe one shown in FIG. 6A. In one embodiment, the table may include a URLcolumn 172 which may simply store the URL string that has beencharacterized. The Category column 174 may store data about the how thatURL has been characterized by database module 114 (as will be describedin detail below). In one embodiment, the URL field may be indexed sothat it may be more quickly searched in real time. Because the list ofcategorized URLs may reached well into the millions of URLs, a fastaccess routine is beneficial.

Referring now to FIG. 6B, the table of uncategorized URLs 147 isprovided (described earlier in connection with FIG. 3). This table maybe populated by URL requests from the workstation 116 which request URLsthat are not present in the categorized URL table 146. As will bedescribed in greater detail below, the gateway server module 120 may beconfigured to query the categorized URL database 146 to determinewhether a requested URL should be blocked. If the requested URL is inthe categorized database 146 the policy module may determine whether toallow the request to proceed to the internet 112. If the requested URLis not found in the categorized URL database, however, it may be addedto the list of uncategorized URLs 176 so that it may be sent to thedatabase management module 114 via the Internet 112 and later analyzedand categorized and downloaded into the database of categorized URLs146.

FIG. 7 is an illustration of various components that may be included inthe database management module 114. As discussed above, the databasemanagement module 114 may be located remotely (accessible via Internet112) from the network 110 and its associated workstations 116. Thedatabase management module may take the form of one or many differenthardware and software components such as a server bank that runshundreds of servers simultaneously to achieve improved performance.

In one embodiment, the database management module 114 may include anupload/download module 178. The upload/download module 178 may be asoftware or hardware component that allows the database managementmodule 114 to send and receive data from the Internet 112 to any numberof locations. In one embodiment, the upload/download module isconfigured to send newly categorized URLs to gateway server modules 120on the Internet 112 for addition to their local URL databases 146.

The database management module 114 may also include a URL/contentdatabase 180. The URL/content database 180 may take the form of a datawarehouse which stores URL strings and information about URLs that havebeen collected by the collection system 182. The URL/content database180 may be a relational database that is indexed to provide quick andeffective searches for data. In certain embodiments, the URL databasemay be a data warehousing application which spans numerous physicalhardware components and storage media. The URL database may include datasuch as URL strings, the content associated with those strings,information about how the content was gathered (e.g., by a honey client,by a customer submission, etc.), and possibly the date in which the URLwas written into the URL/content database 180.

The database management module 114 may further include a training system184. The training system 184 may be a software/hardware module which isused to define properties and definitions that may be used to categorizeweb-based content. The database management module 114 may furtherprovide a scoring/classification system 186 which utilizes thedefinitions and properties created by the training system 184 to providea score or classification (e.g., a categorization) to web content sothat the categorization may be delivered via the upload/download module178 to gateway server modules 120.

With reference now to FIG. 8, a more detailed view of the collectionsystem 182 is provided. The collection system 182 may include acollection module 190 which is coupled (either directly or indirectly)to a data mining module 192. The collection module 190 may be used bythe database management module 114 to collect data for the URL database180 about URLs that have not been categorized. In addition to URLs, theURL database 180 may also store content associated with URLs. Thecollection module may also be used to collect URLs for additionalanalysis by other system components. The collection module 190 may beassociated with one or more collection sources 194 from which it maycollect data about URLs. Collection sources may take various forms. Insome embodiments, the collection sources 194 may include active andpassive honeypots and honey clients, data analysis of logging databases144 stored on gateway server module 120 to identify applications, URLsand protocols for collection. The collection sources may also bewebcrawling applications that search the Internet 112 for particularkeywords or search phrases within page content. The collection sources194 may also include URLs and IP addresses data mined from a DNSdatabase to identify domains that are associated with known malicious IPaddresses. In some embodiments, URLs for categorization may be collectedby receiving malicious code and malicious URL samples from otherorganizations who share this information. In yet other embodiments, URLsmay be collected via e-mail modules configured to receive tips from thepublic at large, much in the way that criminals are identified throughcriminal tip hotlines.

Referring now to FIG. 9, a more detailed view of the collection module190 is provided. The collection module 190 may include varioussubcomponents that allow it to effectively utilize each of thecollection sources described above. The collection module 190 mayinclude a search phrase data module 197 and a expression data module198; The search phrase data module 197 collects and provides searchphrases that may relevant to identifying inappropriate content. Theexpression data module may include various types of expressions such asregular expressions, operands, or some other expression. The searchphrase data module 197 and the expression data module 198 each mayinclude updatable record sets that may be used to define the searchparameters for the web crawling collection source 194. The collectionmodule 190 may also include a priority module 200. The priority module200 may take the form of a software process running within thecollection system 182, or it may run as a separate process. The prioritymodule may be used to prioritize the data collected by the collectionmodule in order to have more potentially dangerous or suspect URLs (ordata) receive close inspection prior to the likely harmless URLs. In oneembodiment, the priority module 200 may assign priority based on thecollection source 194 from which the URL is received. For example, if aURL is received from a customer report, it may be designated with ahigher priority. Similarly, if the URL is received from a web crawleraccessing a domain or IP address or subnet known to host maliciouscontent in the past, the URL may receive a high priority. Similarly, apotentially dangerous website identified by a honey client (discussed infurther detail below) may also receive a high priority. The collectionmodule 190 may also include a data selection module 202 which may workwith the priority module 200 to determine whether identified URLs shouldbe tagged as candidate URLs for categorization. In one embodiment, thedata selection URL may provide a user interface for receiving searchparameters to further refine the prioritized data by searching for databased on priority and content.

As indicated above, the collection module may also include a datadownload module 204. The data download module 204 may be configured toidentify URLs to visit and to download data and content from the visitedURLs. The data download module may work in conjunction with varioussubsystems in the collection module to retrieve data for the URLdatabase 180. One such subsystem is the webcrawler module 206. Thewebcrawler module 206 may be a software application configured to accesswebsites on the Internet 112 by accessing web pages and followinghyperlinks that are included in those pages. The webcrawler module 206may be configured with several concurrent processes that allow themodule to simultaneously crawl many websites and report the visited URLsback to the URL database 180 as will be discussed in further detailbelow. The collection module 190 may also include a honey client module208. The honey client module 208 is a software process configured tomimic the behavior of a web browser to visit websites in such a mannerthat is inviting to malicious code stored within the visited pages. Thehoney client module 208 may visit the web sites and track the behaviorof the websites and download the content back to the URL database 180for further analysis.

The download module 204 may also include a third party supplier module212 which is configured to receive URLs and associated content fromthird parties. For example, the third party module 212 may be configuredto provide a website which may be accessed by the general public. Themodule may be configured to receive an input URL string which may thenbe entered into the URL database 180. In some embodiments, the thirdparty module may also be configured to receive e-mails from private orpublic mailing lists, and to identify any URL data embedded within thee-mails for storage in the URL database 180.

The download module may also include a gateway server access module 210.The gateway server access module is a software component or program thatmay be configured to regularly access the logging database 144 on thegateway server module 120 to download/upload all of the newlyuncategorized web content identified by the logging database 144.

Referring back to FIG. 8, the collection system may also include a datamining module 192. The data mining module 192 may be used to obtainadditional data about URLs stored in the URL database 180. In manyinstances, the information supplied by the collection sources 194 to thecollection module 190 and URL database 180 is limited to nothing morethan a URL string. Thus, in order for the system to effectivelycategorize the content within that URL, more data may be necessary. Forexample, the actual page content may need to be examined in order todetermine whether there is dangerous content embedded within the URL.The data mining module 192 is used to collect this additional necessarydata about the URLs, and will be discussed in further detail below.

FIG. 10 provides a more detailed view of a honey client system 208. Thehoney client system 208 includes control servers 220. The controlservers 220 are used to control a plurality of honey miners 222 whichare configured to visit web sites and mimic human browser behavior in anattempt to detect malicious code on the websites. The honey miners 222may be passive honey miners or active honey miners. A passive honeyminer is similar to a web crawler as described above. However, unlikethe web crawler above which merely visits the website and reports theURL links available from that site, the passive honey miners may beconfigured to download the page content and return it to the controlservers 220 for insertion into the URL database 180. The honey miners222 may be software modules on a single machine, or alternately, theymay be implemented each on a separate computing device.

In one embodiment, each control server may control 17 passive honeyminers 222. The control servers 220 may extract or receive URLs from theURL database 180 which need additional information in order to be fullyanalyzed or categorized. The control servers 220 provide the URLs to theminers which in turn review the URLs and store the collected data. Whena passive miner 222 is finished with a particular URL, it may requestanother URL from its control server 222. In some embodiments, the miners222 may be configured to follow links on the URL content so that inaddition to visiting URLs specified by the control server 220, theminers may visit content that it linked to those URLs. In someembodiments, the miners 222 may be configured to mine to a specifieddepth with respect to each original URL. For example, the miners 222 maybe configured to mine down through four layers of web content beforerequesting new URL data from the control server 220.

In other embodiments, the control servers 220 may be configured tocontrol active honey miners 222. In contrast to the passive honey minerswhich only visit web sites and store the content presented on the sites,the active honey miners 222 may be configured to visit URLs and run orexecute the content identified on the sites. In some embodiments, theactive honey miners 222 include actual web browsing software that isconfigured to visit websites and access content on the websites via thebrowser software. The control server 220 (or the honey miners themselves222) may be configured to monitor the characteristics of the honeyminers 222 as they execute the content on the websites they visit. Inone embodiment, the control server 220 will record the URLs that arevisited by the honey miners as a result of executing an application orcontent on the websites visited. Thus, active honey miners 222 mayprovide a way to more accurately track system behavior and discoverpreviously unidentified exploits. Because the active honey miners exposethemselves to the dangers of executable content, in some embodiments,the active honey miners 222 may be located within a sandbox environment,which provides a tightly-controlled set of resources for guest programsto run in, in order to protect the other computers from damage thatcould be inflicted by malicious content. In some embodiments, thesandbox may take the form of a virtual machine emulating an operatingsystem. In other embodiments, the sandbox may take the form of actualsystems that are isolated from the network. Anomalous behavior may bedetected by tracking in real-time, changes made to the file system onthe sandbox machine. In some embodiments, the code executed by theactive honey miners 222 may cause the machine on which they are runningto become inoperable due to malicious code embedded in the webpagecontent. In order to address this issue, the control server may controla replacement miner which may step in to complete the work of a honeyminer 222 which is damaged during the mining process.

Referring now to FIG. 11, an example of a set of URL-related data thathas been collected by the collection system is provided. Although aparticular example of collected data is provided, one of skill in theart will appreciate that other data might be collected in addition tothe data provided in this example. Included in the collected data is anIP address 230 for the URL. The IP address 230 may be used to identifywebsites that are hosting multiple domains of questionable content underthe same IP address or on the same server. Thus, if a URL havingmalicious content is identified as coming from a particular IP address,the rest of the data in the URL/content database 180 may be mined forother URLs having the same IP address in order to select them and morecarefully analyze them. The collected URL data may also include a URL232 as indicated by the second column in FIG. 11. In instances where thedata is collected using a mining process such as the honey clientprocess described above, the URL 232 may often include various pagesfrom the same web domains, as the miners may have been configured tocrawl through the links in the websites. The collected data may alsoinclude the page content 234 for a particular URL. Because the contentof a URL may be in the form of graphics, text, applications and/or othercontent, in some embodiments, the database storing this URL data may beconfigured to store the page content as a binary large object (blob) orapplication objects in the data record. However, as some web pagescontain text exclusively, the page content 234 may be stored as text aswell. In some embodiments, the collection routine may be configured todetermine whether the URL contains executable content. In theseinstances, the resultant data set of collected data may include anindication of whether the URL has executable content 236 within its pagecode. This information may be later used in selecting data from theURL/content database 180 has candidate data for analysis.

FIG. 12 is a block diagram illustrating the scoring and categorizationmodule 186 from FIG. 7. In one embodiment, the scoring andcategorization module 168 includes a properties database 320, a, aprocessed web page properties database 324, a definitions database 326,a static content classification module 328 and a content scoring module330. In one embodiment, the scoring and categorization module 186includes an active analysis module 332. The content analysis module 322receives one or more candidate URLs from the URL database 180 andidentifies properties from the properties database 320 that it findsassociated with each candidate URL. The values and/or counts of theproperties for each URL are stored in the processed web page propertiesdatabase 324. The static content classification module 328 queries theprocessed web page properties database 324 based on definitions from thedefinitions database 326 to associate categories with the candidateURLs. The content scoring module 330 may further associate a score witheach URL that can be used to further categorize or to change thecategories identified by the static content classification module 328.In one embodiment, the content scoring module 330 may identify candidateURLs for processing by the active analysis module 332. The activeanalysis module 332 downloads and executes any active content toidentify behavior properties associated with the URL. These propertiesmay then be provided to the content scoring module to further categorizethe candidate URLs, e.g., change their categories, or add additionalcategories.

For example, a URL that is processed by the content analysis module 322may receive a “malicious” category. The content scoring module 330 maythen associate a score, e.g., a low score, with the URL that isindicative of the URL not being malicious. To resolve, the contentscoring module 330 may provide the URL as a candidate URL to the activeanalysis module 332 to identify further properties or a behavior scorethat can be used by the content scoring module 330 to determine whetherthe “malicious” category is appropriate.

The properties database 320 includes keywords, regular expressions, andother web page properties that can be used to categorize web pages.Properties may also be values associated with the web page such as HTTPrequest header data or other meta data associated with the web page. Forexample, properties may includes keywords to be identified in thedocument such as “<javascript>,” “<object>,” regular expressions such as“data=.*\.txt” (e.g., the keyword “data=” followed by an arbitrarylength string followed by “.txt”), or the content-type of the data fromthe HTTP header. FIG. 13A is an example of a properties database thatincludes the property and an additional field identifying the type ofproperty, e.g., a keyword or a regular expression. In the illustrativedatabase, a property ID field is used to provide a unique (within thedatabase) identifier for each property. In other embodiments, othersuitable types of keywords may be used.

In one embodiment, the content analysis module 322 receives candidateURLs from the URL database that have been identified by the collectionsystem 182. The content analysis module receives the content and otherdata associated (such as the HTTP header) with the URLs and identifiesone or more of the properties in the properties database 320 that areassociated with the candidate web pages and stores data relating tothose properties in the processed web page properties database 324. Thecontent analysis module 322 may receive the content of the candidate webpages from the URL database or it may download the data itself. In oneembodiment, the honey client module 208 obtains and stores the contentof each candidate web page in the URL database. In another embodiment,the content analysis module 322 downloads the content of the candidateweb pages as part of processing the web page for properties.

In general, the properties database 320 stores the properties andsufficient information to identify the properties associated with a webpage. For example, for keyword or regular expression properties, theproperties database 320 may store the keyword or regular expression. Incontrast, the processed web page properties database 324 may storecounts of the keyword or regular expression found to be associated witheach web page by the content analysis module 322. For regularexpressions, depending on the embodiment, either a count of matchingexpressions or the matching expressions themselves, or both may bestored in the processed web page properties database 324. For example,for a particular web page, the processed web page properties database324 might store the value 3 referring to the number of times that theproperty “<javascript>” appears in the page, 0 for the number of timesthe property “<object>” appears, and“data=http://www.example.url/example.txt.” for the regular expressionproperty “data=.*\.txt.”

FIG. 13B illustrates one embodiment of table in the processed web pageproperties database 324 in which the example properties of FIG. 13A havebeen processed with respect to several web pages. In the illustratedembodiment, the database includes two tables, one relating URLs tounique (within the database) identifiers and a second relating the URLidentifiers with properties associated with that URL. In the illustratedembodiment, the table includes an entry or row for each property of theweb content data associated with the URL. In one embodiment, thedatabase also includes numeric values for each property/URLcorresponding to the keyword properties indicate the number of timesthat the particular property was found in the web page. The database,for example in the URL/property table, may also include the actualexpression matching a regular expression property for the URL. In oneembodiment, the keyword properties can be searched in the page body andin the header or other metadata. In one embodiment, only the page bodyis searched. In yet another embodiment, the property may be associatedwith data, e.g., in the properties database 320, that indicates whatdata to process in identifying the property in a web page.

In one embodiment, the static content classification module 328 accessesweb page properties database 324 and compares the properties for one ormore web pages with definitions from the definitions database 326. Whena web page matches a particular definition, the web page is identifiedwith one or more categories associated with the definition. In oneembodiment, these categories are stored in the URL database inassociation with the URL. In one embodiment, each definition isexpressed in terms of one or more properties of the web page. In oneembodiment, definitions are expressed as first order logical operationsrelating one or more of the properties. In one embodiment, terms of thedefinition are comprised of comparisons between web page properties orbetween properties and values (including constant values). For example,a definition might include an expression such as“property_(—)1”=“property 2” AND occurrences of property_(—)3>5. Inaddition to comparisons, terms may include other operations on web pageproperties such as mathematical, string, or any other suitablecomputational expression. For example, a simple definition can be“data=,*\.txt”=“data=xyx333.txt”, which matches any web page have aspart of its content the string “data=xyx333.txt” (which matches theregular expression property “data=,*\.txt”). More complex definitionsmay comprise logical operations on the terms. Such logical operationsmay include AND, OR, NOT, XOR, IF-THEN-ELSE, or regular expressionmatches on the properties. In one embodiment, the definitions may alsoinclude or correspond to database query expressions such as standard SQLdatabase comparison functions and logical operations. In one embodiment,definitions may include executable code such as scripts or references toexecutable programs or scripts that at least partially determine aclassification for a URL. FIG. 13C illustrates an exemplary portion of adefinitions database 326 according to one embodiment. As used herein,categories can refer to any type of classification. For example, acategory may be merely a classification that indicates that furtherprocessing or analysis be performed for the URL to identify a categoryfor the URL.

In one embodiment, the content scoring module 330 further analyzes webpages and assigns a score to the web page associated with one or morecategories. In one embodiment, the score may be based on a weightedcombination of the number of times that keywords are found in the webpage. In one embodiment, the weights are stored in the propertiesdatabase in association with the corresponding property.

In another embodiment, the scores may be determined based on informationabout the URL of the web page. For example, scores may be assigned toparticular based on a database of internet addresses and/or domainnames.The database may assign scores to entire subnetworks (e.g., alladdresses matching 128.2.*.* may have a particular score). Such networksor subnetworks help identify a web site as being based in a particularcountry or with a particular service provider. This has been found to beuseful in scoring because certain countries and service providers havebeen correlated with certain types of web content due to different lawsor lax enforcement of laws. The scoring system of networks orsubnetworks may be based on the relative number of URLs in particularnetworks or domains that have a particular category. For example, if 95%of the URLs for a particular network in the URL database 180 areclassified as malicious, new URLs may be given a high score. In oneembodiment, URLs with scores above a threshold are identified with acategory, e.g., malicious, regardless of, or in addition to, thecategory identified by content analysis of the web page. In oneembodiment, multiple scores associated with different categories areassigned to each URL, and the categories corresponding to each scoreabove a given threshold are identified with the URL. In one embodiment,multiple threshold are employed. For example, URLs having scores aboveone threshold value automatically are classified based on the score. Inone embodiment, URLs having scores that are below the first thresholdbut above a second threshold are communicated to a human analyst forclassification. In one embodiment, the content scoring module 330communicates such URLs to the active analysis module 332 for additionalanalysis.

One embodiment may include a scoring and categorization system such thatillustrated in U.S. Pat. No. 6,606,659, entitled “System and method forcontrolling access to internet sites,” which document is incorporated byreference in its entirety.

In one embodiment, the active analysis module 332 executes activecontent of a web page to identify its behavior properties. Theseproperties may then be used to score and classify the web page. In oneembodiment, one or more of the static content classification module 328and the content scoring module 330 identifies URLs for processing by theactive analysis module 332. After receiving candidate URLs, the activeanalysis module 332 may provide a behavioral score or data associatedwith one or more behavior properties (e.g., a property such as “writesto registry”) to the content scoring module for further categorization.

FIG. 14 is a block diagram illustrating one embodiment of the trainingmodule 184 from FIG. 7. In one embodiment, the training module includesan analysis tasking module 352 that identifies web pages or URLs havingcontent, such as active content, for which additional categories aredesired. In one embodiment, the collection module 190 identifies URLshaving active content. In another embodiment, an external source, suchas security researchers, identify particular URLs having active contentthat has been identified with one or more categories, e.g., keyloggers,viruses, malicious content, worms, etc. In one embodiment, these may bestored in the URL database 180. In one embodiment, the tasking module352 maintains a database of such URLs (not shown). In one embodiment,the tasking module 352 database maintains a priority for these URLs andpresents them to an analyst based on the priority.

A property identification module 354 identifies properties of the webpage and definitions based on those properties that categorize the webpage. In one embodiment, the properties identification module 354provides an interface for a human analyst to apply particular rules ordefinitions to a URL using the scoring and classification module 186. Inaddition, in one embodiment, the property identification module 354 mayprovide an interface for the analyst to identify the URL as a candidatefor the active analysis module 332 of FIG. 10 to perform behavioralanalysis of the URL to receive additional data for classifying the URLback from the active analysis module 332. The property identificationmodule 354 may then provide this data to the analyst. In one embodiment,the analyst analyzes URL data from the scoring and classification module186, including the active analysis module 332, to help identifyproperties and definitions that properly classify the URL and, wherepossible, other URLs that refer to similarly classified content. In oneembodiment, property identification module 354 provides these newlyidentified properties and definitions to a database update module 356that stores the new definitions and properties to the propertiesdatabase 320 and the definitions database 326.

FIG. 15 is a block diagram illustrating one embodiment of the activeanalysis module 332 from FIG. 12. In one embodiment, the active analysismodule 332 includes a sandbox module 370 in which URLs are downloadedand any active content executed as would occur on a typical workstation116. The sandbox module 370 transparently monitors the state of thecomputer to identify behavior of the web content affecting, for example,one or more of spawned processes, network access, processor usage,memory usage, use of system resources, file system access ormodification, and registry access or modification.

A behavioral analysis module 372 compares the monitored actions from thesandbox module with a list, a database, or rules that characterize themonitored actions. In one embodiment, these characterizations defineproperties of the URL that are subsequently analyzed by the staticcontent classification module 328 of FIG. 12. In another embodiment, anactive scoring classification module 374 may use scores associated withbehavioral properties to determine a score for the URL. In oneembodiment, the score is a weighted score of these properties. Thisscore may be used to classify the URL or be communicated to the contentscoring module for classification. In another embodiment, rules ordefinitions, such as those from the definitions database 332 are appliedto the behavioral properties of the URL (and, in one embodiment, theprocessed web page properties 324) to identify one or more categoriesassociated with the URL.

Description of Methods of Use and Operation

Depending on the embodiment, the acts or events of the methods describedherein can be performed in different sequences, can be merged, or can beleft out all together (e.g., not all acts or events are necessary forthe practice of the method), unless the text specifically and clearlystates otherwise. In addition, the methods described herein can includeadditional acts or events unless the text specifically and clearlystates otherwise. Moreover, unless clearly stated otherwise, acts orevents may be performed concurrently, e.g., through interrupt processingor multiple processors, rather than sequentially.

As discussed above in connection with FIG. 3, in some embodiments, thegateway server module 120 may be configured to control access to certainURLs based on data stored in the categorized URL database 146. FIG. 16is a flowchart describing an embodiment in which the gateway servermodule handles a request from a workstation 116.

At block 1200, the workstation 116 requests a URL from the Internet 112.This request is intercepted at the Internet gateway and forwarded to thegateway server module 120 at block 1202. At block 1204, the categorizedURL database 146 is queried to determine if the requested URL is storedin the database 146. If the requested URL is found as a record in thedatabase, the process moves on to block 1206, where it analyzes the URLrecord to determine whether the category of the URL is one that shouldbe blocked for the workstation user. If the category is blocked, theprocess skips to block 1212 and the request is blocked. If the categoryis not blocked, however, the request is allowed at block 1208.

If the requested URL is not found as a record in the categorized URLdatabase 146 at block 1204, the system proceeds to block 1210. At block1210, the system determines how to handle the uncategorized content. Insome embodiments, the system may utilize the policy module 142 to makethis determination. If the gateway server module 120 is configured toblock requests for uncategorized content, the process moves to block1212, and the request is blocked. If, on the other hand, the module isconfigured to allow these types of uncategorized requests, the processmoves to block 1208, where the request is allowed to proceed to theInternet 112.

In some embodiments, the request of URL data may result in new recordsbeing added to the logging database 144. These records may be latertransferred to the database management module 114 for further analysis.Referring now to FIG. 17, another flowchart describing a process bywhich the gateway server module may handle a URL request is provided. Atblock 1300, the gateway server module 120 receives a request for a URL.As noted above, this request may come from a workstation 116. At block1302, the URL is then compared against the categorized URL database 146,and the system determines at block 1304 whether the requested URL is inthe categorized URL database.

If the URL is already in the categorized URL database 146, the processskips to block 1308. If the requested URL is not found in thecategorized URL database 146, however, the process moves to block 1306where the URL is inserted into the uncategorized URL database 147. (Insome embodiments, the logging database 144 and the uncategorized URL 147database may be the same database.) After inserting the URL into thedatabase, the method proceeds to block 1308. At block 1308, the policydatabase is checked for instructions on how to handle the received URL.Once the policy module 142 has been checked, the logging database 144 isupdated to record that the URL has been requested at block 1310. Afterupdating the logging database 144, if the workstation 116 is permittedto access the URL by the policy database, the process moves to block1314 and the URL request is sent to the Internet 112. If, however, thepolicy database does not allow the request, the process skips to block1316 and the request is blocked.

In some embodiments, the gateway server module 120 may performcollection activities to lessen the burden on the collecting system 182of the database management module 114. FIG. 18 provides an example of asystem in which the gateway server collection module 140 is used tocollect data about an uncategorized URL. At block 1400, the gatewayserver module receives a request for a URL. Next, at block 1402, therequested URL is compared against the categorized URL database. If thesystem determines that the requested URL is in the URL database at block1404, the process moves to block 1410, where the request is eitherforwarded to the Internet 112 or blocked depending on how the URL iscategorized.

If the requested URL is not in the categorized URL database 146, theprocess moves to block 1406 where the URL is sent to the gatewaycollection module 140. Next, at block 1408, the collection module 140collects URL data about the requested URL. In some embodiments, thisdata may be stored in the uncategorized URL database 147. Alternatively,this data may simply be forwarded to the database management module 114via the Internet 112. Once the data has been collected and stored, theprocess moves to block 1410 where the URL request is either allowed orblocked based on the policies indicated in the policy module 142.

As discussed previously, uncategorized URL data may be sent from thegateway server module 120 to the database management module 114 forfurther analysis so that the URL may be categorized and added to thecategorized URL database 146. However, because the volume ofuncategorized data is so large at times, it may not be possible tocategorize all of the received data without compromising accuracy orspeed. As a result, in some instances, it may be desirable to identifycandidate URLs within the uncategorized data that are most likely topresent a threat to workstations 116 and networks 110.

FIG. 19 provides an example of a method for identifying candidate URLsfor further analysis. The method starts with a URL being received intothe collection system 182 of the database module 114. At block 1502, theURL or application is preprocessed to determine whether it carries aknown malicious data element or data signature. Next, at block 1504, ifthe system determines that the URL includes a known malicious element,the process skips to block 1514 where the URL is tagged as a candidateURL and sent to the training system 184 for further analysis. If theinitial analysis of the URL in block 1504 does not reveal a maliciouselement, the process moves to block 1506, where the URL is added to adatabase of potential candidate URLs. Next, at block 1508, the datamining module 192 is configured to select URLs from sources 194 (ofwhich the database of potential candidate URLs is one) based onpreconfigured conditions such as attack strings, virus signatures, andthe like. The data set including all of the data sources 194 is thensent to the data mining module 192 at block 1510, where each URL isanalyzed by the data mining module 192 at block 1512. If the URLsatisfies the defined preconfigured conditions, the process moves tobock 1514 where the URL is tagged as a candidate URL and sent on to thescoring/classification system 186 for additional analysis. If, however,the URL does not meet the conditions specified for converting it to acandidate URL, the method proceeds to block 1516 and the URL is nottagged as a candidate. Although this embodiment is described in thecontext of URL candidate classification, one of skill in the art willreadily appreciate that applications may be similarly analyzed andtagged as candidates using the process described above.

As discussed above, one of the challenges to collecting and analyzingInternet data to determine whether it includes harmful active content isthe sheer volume of data that must be collected and analyzed. In yetanother embodiment, the data mining module 192 may be used to addressthese issues by collecting large volumes of relevant data utilize systemresources effectively and efficiently. Referring now to FIG. 20, a moredetailed block diagram of the data mining system 192 is provided. Thedata mining system 192 may take the form of a software module that runsa plurality of asynchronous processes to achieve maximum efficiency andoutput. The data mining system 192 may include a plug-in module 242which receives configuration parameters which provide instruction on howinputted data should be handled. In one embodiment, the instructionsreceived by the plug-in module may take the form of an HTTP protocolplug-in that provide parameters for the data mining system 192 toreceive URL data and analyze and supplement the data based on variousHTTP-related instructions implemented by the data mining system on theURL data. In another embodiment, the plug-in may be geared toward miningsome other protocol such as FTP, NNTP, or some other data form.

The data mining system 192, which may also be used to implement passivehoney clients, also include a pool 246 of dispatchers 248. Thedispatchers 248 are individual asynchronous processing entities thatreceive task assignments based on the data input (for analysis) into thedata mining system and the configuration data received by the plug-inmodule 242. The pool 246 is a collection of the dispatchers that iscontrolled by a driver 244. The driver 244 is a managing mechanism forthe pool. The driver 244 may be configured to monitor the activity ofthe dispatchers 248 in the pool 246 to determine when to send additionaldata into the pool 246 for mining and analysis. In one embodiment, thedriver may be configured to send new data units into the pool 246whenever any dispatchers 248 are idle. In one embodiment, the driver 244may be utilized as a control server for managing honeyclient miners 222as described above in connection with FIG. 10. The pool 246 may deliverthe data unit to the idle dispatcher 248. The dispatcher 248 reads theplug-in configuration and performs actions in accordance with plug-in242.

In one embodiment, the plug-in module may receive an HTTP plug-in. TheHTTP plug-in may be configured to receive input data in the form of URLstrings about which the data mining system 192 will obtain additioninformation such as the page content for the URL, HTTP messages returnedby the URL when accessed (such as “4xx—file not found” or “5xx—servererror”). The plug-in may further specify a webcrawling mode in which thedispatches, in addition to collecting page content, also add URL linkswithin the URL content to the URL data set to be analyzed.

FIG. 21 is a flowchart illustrating one embodiment of a method 2000 ofcategorizing URLs within the database management module 114. The method2000 begins at a block 2002 in which properties are developed that canbe used to categorize web pages. In one embodiment, the training module184 is used to develop the properties in the properties database 320. Inone embodiment, developing the properties includes developingdefinitions, e.g., expressions relating one or more properties, andstoring the definitions in the definitions database 326. Next at a block2004, web pages are identified for content analysis. In one embodiment,the collections module 190 identifies web pages for content analysis. Inone embodiment, web pages having properties or other indicia of activecontent are identified for content analysis.

Moving to a block 2006, the content analysis module 322 identifies oneor more properties associated with each of the identified web pages.Functions of block 2006 are described in more detail hereafter withreference to FIG. 22. Proceeding to a block 2010, the static contentclassification module 328 identifies web pages with one or morecategories based at least partly on properties. In one embodiment, thestatic content classification module 328 compares definitions from thedefinitions database 326 with the properties of each web page toidentify its properties. In one embodiment, the categories include thoseindicative of whether the web page is associated with active content. Inone embodiment, the categories include those indicative of types ofactive content, e.g., malicious, phishing sites, keyloggers, viruses,worms, etc., associated with or referenced by the web page. In oneembodiment, the active content is included in the body of the web page.In one embodiment, the active content is referenced in a link or ActiveXobject element of the web page. In one embodiment, active contentincludes interactive “phishing” sites that include content tending tomislead users into providing credentials or other sensitive, private, orpersonal information. In one embodiment, the scoring module 330 furtherscores and classifies the web pages. Moving to a block 2012, thecategories associated with the web pages are stored in the URL database.In one embodiment, the upload download module 178 of FIG. 7 distributesthe new URL categories to one or more gateway server modules 120 orworkstations 116 (both of FIG. 1). In one embodiment, one or more blocksof the method 2000, e.g., blocks 2006-2012, may be performed eithercontinuously as new URLs are received by the collections module 190. Inone embodiment, one or more blocks of the method 2000, e.g., blocks2006-2012, may be performed periodically.

FIG. 22 is a flowchart illustrating one embodiment of a method ofperforming the function of the block 2006 of FIG. 21. The method beginsat a block 2020 in which the content analysis module 322 receives a listof web page URLs in the URL database 180. In one embodiment, thecollection module 190 provides the list of candidate URLs. Next at ablock 2022, for each URL, the content analysis module 322 receivesdownloaded web page content. In one embodiment, the collection module190 downloads the content and stores it in the URL database 180 fromwhich the content analysis module 322 accesses the content. In anotherembodiment, the content analysis module 322 downloads and processes thecontent. Moving to a block 2024, the content analysis module 322accesses properties from the properties database 320. Next at a block2026, the content analysis module 322 identifies properties that areassociated with each of the web pages based at least partly on thecontent of each of the web pages. In one embodiment, the contentanalysis module 322 scans the content to identify string, keyword andregular expression properties from the properties database 320. In oneembodiment, the content analysis module 322 may also decode contentprior to, and/or after, scanning for properties. For example, thecontent analysis module 322 may decode web content such as URL-encodedportions of URLs or hex-coded web addresses prior to scanning to helpprevent keywords from being hidden by encoding or partially encoding thekeywords. Proceeding to a block 2028, the content analysis module 322stores the identified properties associated with each web page in theprocessed web page properties database 324.

FIG. 23 is a flowchart illustrating one embodiment of a method ofperforming the function of the block 2010 of FIG. 21. The method beginsat a block 2042 in which the static content classification module 328accesses definitions indicative of web page categories from thedefinitions database 326. Next at a block 2044, for each definition, thestatic content classification module 328 identifies one or more queriesassociated with each definition against the processed web pageproperties database 324. In one embodiment, the queries comprises SQLqueries.

Moving to a block 2046, the static content classification module 328compares the properties of the URLs in the web page properties databaseto the query to identify URLs matching the query. In one embodiment, thestatic content classification module 328 performs the comparison byexecuting the one or more identified database queries against theprocessed web page properties database 324. Next at a block 2050, thestatic content classification module 328 compares any identified URLswith the definition to identify any of the identified URLs that matchthe definition. In one embodiment, this comparison includes comparingthe results of the database query using additional executableinstructions, such as a Perl script, to identify matching URLs.Proceeding to a block 2052, the static content classification module 328categorizes the identified URLs based on the definition. In oneembodiment, each definition is associated with a single category. Inanother embodiment, each definition is associated with severalcategories that are each identified with the URL. In yet anotherembodiment, the definition may include logical expresses that identifyone or more categories to identify with the URL. For example, anif-then-else expression may identify different categories depending onthe result of the if expression. In one embodiment, the content scoringmodule further scores the URL. Based on the score, the same, different,or additional categories may be identified with the URL. Next at a block2054, the static content classification module 328 stores the categoriesof each URL to a categorized web page database. In one embodiment, theURL database 180 includes the categorized web page database.

FIG. 24 is a flowchart illustrating one embodiment of a method ofperforming the function of the block 2002 of FIG. 21 as part ofidentifying the properties used in categorizing URLs in the methods ofFIGS. 22 and 23. The method begins at a block 2062 in which the analysistasking module 352 of FIG. 14 receives active content data or URLsassociated with active content. Next at a block 2064, propertyidentification module 254 identifies properties that distinguish thetarget URLs related to the active content data from other URLs andidentify one or more categories associated with the target URLs. In oneembodiment, the scoring and classification system 186 is used to helpidentifies these properties. In addition, definitions comprising one ormore of the properties may be identified that distinguish the targetURLs that are associated with a particular category from other URLs thatshould not be associated with that category. Moving to a block 2068, thedatabase update module 356 stores the properties, definitions, andcategories in the properties database 320 and the definitions database326. These updated properties and definitions are thus made availablefor processing URLs using, for example, the method illustrated in FIG.21.

As used herein, “database” refers to any collection of stored datastored on a medium accessible by a computer. For example, a database mayrefer to flat data files or to a structured data file. Moreover, it isto be recognized that the various illustrative databases described inconnection with the embodiments disclosed herein may be implemented asdatabases that combine aspects of the various illustrative databases orthe illustrative databases may be divided into multiple databases. Forexample, one or more of the various illustrative databases may beembodied as tables in one or more relational databases. Embodiments maybe implemented in relational databases, including SQL databases such asmySQL, object oriented databases, object-relational databases, flatfiles, or any other suitable data storage system.

Those of skill will recognize that the various illustrative logicalblocks, modules, circuits, and algorithm steps described in connectionwith the embodiments disclosed herein may be implemented as electronichardware, computer software, or combinations of both. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present invention.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein may be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.A software module may reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. Anexemplary storage medium is coupled to the processor such the processorcan read information from, and write information to, the storage medium.In the alternative, the storage medium may be integral to the processor.The processor and the storage medium may reside in an ASIC. The ASIC mayreside in a user terminal. In the alternative, the processor and thestorage medium may reside as discrete components in a user terminal.

In view of the above, one will appreciate that embodiments of theinvention overcome many of the longstanding problems in the art byproviding an efficient means of processing the large numbers of URLsthat are available on the Internet to identify categories for URLs,particularly those that have active content. URLs having many types ofactive content may be difficult even for a human analyst to categorizebecause the relevant properties may be buried in executable code,including scripts, or in parameters to ActiveX components. The use ofproperties and definitions that can be efficiently processed allowsActiveX content to be effectively identified by an automatic process.Furthermore, by storing the properties of web pages in a database forlater querying, large numbers of URLs can immediately be categorizedbased on these stored properties when a new definition of active contentis identified.

While the above detailed description has shown, described, and pointedout novel features of the invention as applied to various embodiments,it will be understood that various omissions, substitutions, and changesin the form and details of the device or process illustrated may be madeby those skilled in the art without departing from the spirit of theinvention. As will be recognized, the present invention may be embodiedwithin a form that does not provide all of the features and benefits setforth herein, as some features may be used or practiced separately fromothers. The scope of the invention is indicated by the appended claimsrather than by the foregoing description. All changes which come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

1. A method of classifying web content, the method comprising: receivingcontent of at least one web page; identifying properties associated withthe web page based at least partly on the content of the web page;storing said properties in a database of web page properties; comparingat least one definition to properties stored in the database of web pageproperties; identifying the web page with at least one definition basedon comparing said definition with said stored properties; andidentifying the web page with at least one category associated with theat least one definition, wherein said category is indicative of activecontent associated with the web page.
 2. The method of claim 1, whereincomparing the web page to the definition comprises performing at leastone database query associated with at least one definition, wherein thequery selects the web page from the database of web page propertiesbased at least partly on the properties of the selected at least one webpage.
 3. The method of claim 1, further comprising: executinginstructions associated with the at least one web page; identifying atleast one behavioral property associated with the web page, whereinidentifying the web page with the at least one category is based atleast partly on the behavior property.
 4. The method of claim 1, whereinidentifying the web page with a category associated with the at leastone definition comprises storing data associating a uniform resourcelocator of the at least one of the web pages with the category.
 5. Themethod of claim 1, wherein the category identifies the at least one webpage as having malicious content.
 6. The method of claim 1, furthercomprising receiving the at least one definition from a database ofdefinitions.
 7. The method of claim 1, wherein at least one of thedefinitions comprises a logical expression.
 8. The method of claim 7,wherein the logical expression comprises at least one term comprising arelationship of at least one web page property to at least one othervalue.
 9. The method of claim 8, wherein the at least one other valuecomprises a constant value.
 10. The method of claim 8, wherein the atleast one other value comprises at least one other web page property.11. The method of claim 1, wherein at least one of said properties isassociated with a string.
 12. The method of claim 1, wherein at leastone of said properties is associated with a regular expression.
 13. Themethod of claim 11, wherein the at least one of said propertiescomprises a number indicative of occurrences within the content of theweb page.
 14. The method of claim 11, further comprising determining ascore associated with the URL of the web page, wherein identifying theweb page with at least one category is based at least partly on thescore.
 15. A system for classifying web content, the system comprising:a database configured to properties associated with web pages; at leastone processor configured to: identify properties associated with a webpage based at least partly on content of the web page; store saidproperties in said database of web page properties; compare at least onedefinition to properties stored in the database of web page properties;identify the web page with at least one definition based on comparingsaid definition with said stored properties; and identify the web pagewith at least one category associated with the at least one definition,wherein said category is indicative of active content associated withthe web page.
 16. The system of claim 15, wherein the processor isconfigured to compare the web page to the definition at least in part byperforming at least one database query associated with at least onedefinition, wherein the query selects the web page from the database ofweb page properties based at least partly on the properties of theselected at least one web page.
 17. The system of claim 15, furthercomprising: a second processor configured to: execute instructionsassociated with the at least one web page; identify at least onebehavioral property associated with the web page, wherein the at leastone processor is configured to identify the web page with the at leastone category based at least partly on the behavior property.
 18. Thesystem of claim 15, wherein the processor is configured to identify theweb page with a category associated with the at least one definition atleast partly by storing data associating a uniform resource locator ofthe at least one of the web pages with the category.
 19. The system ofclaim 15, wherein the category identifies the at least one of the webpages as having malicious content.
 20. The system of claim 15, furthercomprising at database configured to store the properties of the webpage.
 21. The system of claim 15, further comprising a databaseconfigured to store the at least one definition.
 22. The system of claim15, wherein the at least one definition comprises a logical expression.23. The system of claim 22, wherein the logical expression comprises atleast one term comprising a relationship of at least one web pageproperty to at least one other value.
 24. The system of claim 23,wherein the at least one other value comprises a constant value.
 25. Thesystem of claim 23, wherein the at least one other value comprises atleast one other web page property.
 26. The system of claim 15, whereinat least one of said properties is associated with a string.
 27. Thesystem of claim 15, wherein at least one of said properties isassociated with a regular expression.
 28. The system of claim 15,wherein the at least one of said properties comprises a numberindicative of occurrences within the content of the web page.